Current (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##          Smokeles             Smoky Wood_and_or_Plant 
##                17                87                 8

Primary analysis

Investigate the association with current (self-reported) fuel type in the LEX study participants, adjusting for known confounders and stove ventilation. The reference group for this analysis would be the smoky coal users. This would be a categorical analysis, and the results would be a p-value from the likelihood ratio (LR) test of a confounder-only model to a model including the exposure variables, as well as p-values for the contrast of each category of coal use (smokeless coal or plant/wood) to that of smoky coal. FDR correction should be used separately for each of these sets. The main interest would be in the coal-specific findings and perhaps less so in the results from the LR test.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2368       0.6340
## Hannum EAA     0.6304       0.6340
## PhenoAge EAA   0.5142       0.6340
## Skin&Blood EAA 0.4887       0.6340
## GrimAge EAA    0.0279       0.2232
## DNAmTL         0.5250       0.6340
## IEAA           0.3694       0.6340
## EEAA           0.6340       0.6340

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\), \(\beta_1\) and \(\beta_2\) with given \(Y\) are shown below. The \(\beta_1\) and \(\beta_2\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2800       0.8200
## Hannum EAA     0.4890       0.8819
## PhenoAge EAA   0.8936       0.8936
## Skin&Blood EAA 0.5512       0.8819
## GrimAge EAA    0.1672       0.8200
## DNAmTL         0.8624       0.8936
## IEAA           0.3075       0.8200
## EEAA           0.6635       0.8847

Linear relation

Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -1.03 0.74 0.17       0.17
## AgeAccelerationResidualHannum       -0.70 0.64 0.28       0.37
## AgeAccelPheno                       -0.06 0.65 0.93       0.93
## DNAmAgeSkinBloodClockAdjAge         -0.08 0.53 0.88       0.88
## AgeAccelGrim                        -0.11 0.47 0.81       0.82
## DNAmTLAdjAge                        -0.02 0.03 0.60       0.60
## IEAA                                -0.98 0.67 0.15       0.15
## EEAA                                -0.72 0.81 0.38       0.47

Cumulative lifetime (self-reported) fuel type

The numbers of observations with each cumulative lifetime fuel type:

## 
##   Mix Smoky 
##    82    37

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3222       0.5415
## Hannum EAA     0.4061       0.5415
## PhenoAge EAA   0.6397       0.7311
## Skin&Blood EAA 0.9331       0.9331
## GrimAge EAA    0.0245       0.1960
## DNAmTL         0.3396       0.5415
## IEAA           0.0940       0.3760
## EEAA           0.2773       0.5415

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \]
where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\) and \(\beta_1\) with given \(Y\) are shown below. The \(\beta_1\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the mix fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3532       0.8414
## Hannum EAA     0.7909       0.8414
## PhenoAge EAA   0.8253       0.8414
## Skin&Blood EAA 0.8414       0.8414
## GrimAge EAA    0.1805       0.7220
## DNAmTL         0.6405       0.8414
## IEAA           0.0759       0.6072
## EEAA           0.6484       0.8414

Linear relation

Use a trend test to estimate a linear relation across use categories (1=mix, 2=Smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -0.88 0.95 0.36       0.36
## AgeAccelerationResidualHannum        0.21 0.80 0.79       0.79
## AgeAccelPheno                        0.18 0.81 0.83       0.83
## DNAmAgeSkinBloodClockAdjAge          0.14 0.69 0.84       0.84
## AgeAccelGrim                         0.75 0.57 0.19       0.19
## DNAmTLAdjAge                        -0.02 0.04 0.64       0.64
## IEAA                                -1.52 0.86 0.08       0.16
## EEAA                                 0.46 1.02 0.65       0.65

Childhood (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##      Mix Smokeles    Smoky     Wood 
##       53        5       47       11

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0412       0.1099
## Hannum EAA     0.1426       0.1901
## PhenoAge EAA   0.2872       0.3282
## Skin&Blood EAA 0.1345       0.1901
## GrimAge EAA    0.0051       0.0408
## DNAmTL         0.4625       0.4625
## IEAA           0.0379       0.1099
## EEAA           0.1276       0.1901

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\), \(\beta_1\), \(\beta_2\), and \(\beta_3\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), and \(\beta_3\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2833       0.6101
## Hannum EAA     0.3813       0.6101
## PhenoAge EAA   0.8336       0.8336
## Skin&Blood EAA 0.7398       0.8336
## GrimAge EAA    0.0146       0.1168
## DNAmTL         0.5919       0.7892
## IEAA           0.1220       0.4880
## EEAA           0.3340       0.6101

Linear relation

Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3 = mix coal, 4=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -0.70 0.50 0.16       0.16
## AgeAccelerationResidualHannum       -0.50 0.42 0.24       0.30
## AgeAccelPheno                       -0.13 0.43 0.77       0.93
## DNAmAgeSkinBloodClockAdjAge          0.02 0.36 0.95       0.95
## AgeAccelGrim                         0.27 0.30 0.37       0.37
## DNAmTLAdjAge                         0.01 0.02 0.62       0.99
## IEAA                                -0.87 0.44 0.05       0.06
## EEAA                                -0.52 0.53 0.33       0.39

Clusters based on model-based exposure estimates at or shortly before the visit (clusCUR6)

The file “LEX_clusCUR6.csv” has information on current pollutant exposures, obtained for the 2 years preceding the visit. To reduce multi-collinearity between exposures, exposure prototypes were derived based on hierarchical cluster analysis in combination followed by principal components analysis. These estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only

Summary the exposure estimates:

##   CUR6_BC_PAH6       CUR6_PAH31         CUR6_NkF        CUR6_PM_RET      
##  Min.   :-1.6472   Min.   :-1.9531   Min.   :-3.0963   Min.   :-1.72458  
##  1st Qu.:-0.5226   1st Qu.:-0.3924   1st Qu.:-0.5918   1st Qu.:-0.54260  
##  Median : 0.7938   Median : 0.3928   Median :-0.3663   Median :-0.30489  
##  Mean   : 0.2134   Mean   : 0.1962   Mean   :-0.1059   Mean   :-0.01324  
##  3rd Qu.: 0.8098   3rd Qu.: 0.6301   3rd Qu.: 0.7448   3rd Qu.: 0.36146  
##  Max.   : 1.6827   Max.   : 2.5950   Max.   : 2.2506   Max.   : 2.60492  
##  NA's   :13        NA's   :13        NA's   :13        NA's   :13        
##     CUR6_NO2           CUR6_SO2      
##  Min.   :-2.58032   Min.   :-3.4207  
##  1st Qu.:-0.44259   1st Qu.:-0.8550  
##  Median : 0.05849   Median :-0.2976  
##  Mean   : 0.18535   Mean   :-0.1312  
##  3rd Qu.: 0.81282   3rd Qu.: 0.2025  
##  Max.   : 2.27519   Max.   : 1.6387  
##  NA's   :13         NA's   :13

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1900       0.2533
## Hannum EAA     0.0239       0.0504
## PhenoAge EAA   0.0210       0.0504
## Skin&Blood EAA 0.1401       0.2242
## GrimAge EAA    0.0085       0.0504
## DNAmTL         0.2939       0.3359
## IEAA           0.4320       0.4320
## EEAA           0.0252       0.0504

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1840       0.2968
## Hannum EAA     0.2914       0.3775
## PhenoAge EAA   0.0241       0.0755
## Skin&Blood EAA 0.0283       0.0755
## GrimAge EAA    0.0263       0.0755
## DNAmTL         0.4823       0.4823
## IEAA           0.3303       0.3775
## EEAA           0.1855       0.2968

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4226       0.4830
## Hannum EAA     0.1558       0.2707
## PhenoAge EAA   0.0209       0.1672
## Skin&Blood EAA 0.1692       0.2707
## GrimAge EAA    0.0806       0.2149
## DNAmTL         0.2510       0.3347
## IEAA           0.6041       0.6041
## EEAA           0.0626       0.2149

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0700       0.0800
## Hannum EAA     0.0037       0.0148
## PhenoAge EAA   0.0093       0.0248
## Skin&Blood EAA 0.0426       0.0800
## GrimAge EAA    0.0651       0.0800
## DNAmTL         0.2166       0.2166
## IEAA           0.0509       0.0800
## EEAA           0.0019       0.0148

Clusters based on model-based exposure estimates accrued before age 18 (clusCHLD5)

The file “LEX_clusCHLD5.csv” has information on estimated pollutant exposures during early childhood. Estimates are available for 5 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CHLD5_X7 – a cluster of 7 air pollutants
CHLD5_X33 – a large cluster of 33 air pollutants
CHLD5_NkF – NkF only
CHLD5_NO2 – NO2 only
CHLD5_SO2 – SO2 only

Summary the exposure estimates:

##     CHLD5_X7          CHLD5_X33         CHLD5_NkF         CHLD5_NO2      
##  Min.   :-2.00581   Min.   :-2.0274   Min.   :-3.7754   Min.   :-2.0155  
##  1st Qu.:-0.53325   1st Qu.:-0.5323   1st Qu.:-0.7656   1st Qu.:-0.5330  
##  Median : 0.10720   Median : 0.1218   Median :-0.2258   Median : 0.2416  
##  Mean   :-0.01945   Mean   : 0.1796   Mean   :-0.1098   Mean   : 0.2268  
##  3rd Qu.: 0.51881   3rd Qu.: 1.1052   3rd Qu.: 0.7312   3rd Qu.: 0.8033  
##  Max.   : 1.77388   Max.   : 1.6650   Max.   : 1.8887   Max.   : 3.5648  
##  NA's   :13         NA's   :13        NA's   :13        NA's   :13       
##    CHLD5_SO2       
##  Min.   :-1.38635  
##  1st Qu.:-0.90192  
##  Median : 0.33208  
##  Mean   : 0.01749  
##  3rd Qu.: 0.44744  
##  Max.   : 1.73751  
##  NA's   :13

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4899       0.5599
## Hannum EAA     0.1305       0.2088
## PhenoAge EAA   0.0576       0.1782
## Skin&Blood EAA 0.0716       0.1782
## GrimAge EAA    0.0120       0.0960
## DNAmTL         0.5692       0.5692
## IEAA           0.4260       0.5599
## EEAA           0.0891       0.1782

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8864       0.8864
## Hannum EAA     0.2901       0.5840
## PhenoAge EAA   0.1416       0.5664
## Skin&Blood EAA 0.3650       0.5840
## GrimAge EAA    0.0208       0.1664
## DNAmTL         0.5466       0.7288
## IEAA           0.6847       0.7825
## EEAA           0.3074       0.5840

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9805       0.9805
## Hannum EAA     0.3840       0.7341
## PhenoAge EAA   0.0700       0.2800
## Skin&Blood EAA 0.1867       0.4979
## GrimAge EAA    0.0634       0.2800
## DNAmTL         0.5506       0.7341
## IEAA           0.7515       0.8589
## EEAA           0.4588       0.7341

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9268       0.9268
## Hannum EAA     0.3979       0.6366
## PhenoAge EAA   0.0655       0.3264
## Skin&Blood EAA 0.2424       0.6366
## GrimAge EAA    0.0816       0.3264
## DNAmTL         0.3621       0.6366
## IEAA           0.7955       0.9091
## EEAA           0.4873       0.6497

Clusters based on model-based lifetime exposure estimates (clusCUM6)

The file “LEX_clus CUM6.csv” has information on estimated cumulative pollutant exposures during the lifecourse. Estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only

Summary the exposure estimates:

##  CUM6_BC_NO2_PM      CUM6_PAH36         CUM6_DlP          CUM6_NkF       
##  Min.   :-2.1989   Min.   :-2.0019   Min.   :-2.4744   Min.   :-2.34566  
##  1st Qu.:-0.5606   1st Qu.:-0.5902   1st Qu.:-1.0232   1st Qu.:-0.84297  
##  Median : 0.2497   Median : 0.2500   Median :-0.5064   Median :-0.21091  
##  Mean   : 0.1138   Mean   : 0.2128   Mean   :-0.2015   Mean   :-0.04154  
##  3rd Qu.: 0.8546   3rd Qu.: 1.1584   3rd Qu.: 0.7657   3rd Qu.: 0.43564  
##  Max.   : 2.6510   Max.   : 1.9951   Max.   : 2.1588   Max.   : 2.54737  
##  NA's   :13        NA's   :13        NA's   :13        NA's   :13        
##     CUM6_RET           CUM6_SO2       
##  Min.   :-2.44171   Min.   :-1.75440  
##  1st Qu.:-0.66614   1st Qu.:-0.68589  
##  Median :-0.20905   Median : 0.09033  
##  Mean   :-0.09119   Mean   :-0.06758  
##  3rd Qu.: 0.40560   3rd Qu.: 0.35109  
##  Max.   : 2.67607   Max.   : 2.10707  
##  NA's   :13         NA's   :13

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4844       0.4844
## Hannum EAA     0.3926       0.4844
## PhenoAge EAA   0.0933       0.3732
## Skin&Blood EAA 0.2405       0.3885
## GrimAge EAA    0.0011       0.0088
## DNAmTL         0.2155       0.3885
## IEAA           0.4703       0.4844
## EEAA           0.2428       0.3885

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5551       0.7401
## Hannum EAA     0.8488       0.8488
## PhenoAge EAA   0.1559       0.4157
## Skin&Blood EAA 0.2862       0.5202
## GrimAge EAA    0.0170       0.1360
## DNAmTL         0.1043       0.4157
## IEAA           0.3251       0.5202
## EEAA           0.7581       0.8488

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6381       0.6381
## Hannum EAA     0.5536       0.6342
## PhenoAge EAA   0.0248       0.1984
## Skin&Blood EAA 0.1313       0.2626
## GrimAge EAA    0.1039       0.2626
## DNAmTL         0.0790       0.2626
## IEAA           0.4141       0.6342
## EEAA           0.5549       0.6342

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6380       0.6380
## Hannum EAA     0.3701       0.4914
## PhenoAge EAA   0.0243       0.1944
## Skin&Blood EAA 0.0878       0.2054
## GrimAge EAA    0.1027       0.2054
## DNAmTL         0.0826       0.2054
## IEAA           0.4300       0.4914
## EEAA           0.3562       0.4914

Clusters based on pollutant measurements (clusMEAS6)

Clusters based on urinary biomarkers (clusURI5)

The file “LEX_clusURI5.csv” has information on measured urinary biomarkers obtained during each visit. Estimates are available for 5 different prototypes (cluster variables) for a total of 163 subjects and 186 visits. The prototypes are labelled as:

URI5_NAP_1M_2M – a cluster of Naphthalene, 1Methylnaphthalene, and 2Methylnaphthalene
URI5_ACE – Acenaphthene only
URI5_FLU_PHE – Fluoranthene and Phenanthrene_anth
URI5_PYR – Pyrene only
URI5_CHR – Baa_Chrysene only

Summary the exposure estimates:

##  URI5_NAP_1M_2M        URI5_ACE         URI5_FLU_PHE         URI5_PYR       
##  Min.   :-2.12630   Min.   :-3.01075   Min.   :-1.97535   Min.   :-2.27382  
##  1st Qu.:-0.65236   1st Qu.:-0.60987   1st Qu.:-0.73402   1st Qu.:-0.25285  
##  Median : 0.07178   Median :-0.09045   Median : 0.03390   Median : 0.06614  
##  Mean   : 0.01441   Mean   :-0.05696   Mean   :-0.04617   Mean   :-0.01833  
##  3rd Qu.: 0.55591   3rd Qu.: 0.68816   3rd Qu.: 0.52169   3rd Qu.: 0.49727  
##  Max.   : 2.69501   Max.   : 1.90667   Max.   : 2.43408   Max.   : 2.09455  
##  NA's   :25         NA's   :25         NA's   :25         NA's   :25        
##     URI5_CHR       
##  Min.   :-3.86540  
##  1st Qu.:-0.48920  
##  Median :-0.03949  
##  Mean   :-0.01131  
##  3rd Qu.: 0.47587  
##  Max.   : 2.39391  
##  NA's   :25

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4408       0.6169
## Hannum EAA     0.4716       0.6169
## PhenoAge EAA   0.0226       0.1808
## Skin&Blood EAA 0.8595       0.8595
## GrimAge EAA    0.0945       0.2520
## DNAmTL         0.0815       0.2520
## IEAA           0.5398       0.6169
## EEAA           0.5041       0.6169

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7166       0.9146
## Hannum EAA     0.8605       0.9146
## PhenoAge EAA   0.0779       0.5412
## Skin&Blood EAA 0.5460       0.9146
## GrimAge EAA    0.2178       0.5808
## DNAmTL         0.1353       0.5412
## IEAA           0.7881       0.9146
## EEAA           0.9146       0.9146

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5630       0.9535
## Hannum EAA     0.7871       0.9535
## PhenoAge EAA   0.1480       0.9535
## Skin&Blood EAA 0.9124       0.9535
## GrimAge EAA    0.8240       0.9535
## DNAmTL         0.5162       0.9535
## IEAA           0.7267       0.9535
## EEAA           0.9535       0.9535

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7266       0.9357
## Hannum EAA     0.8797       0.9357
## PhenoAge EAA   0.1130       0.9040
## Skin&Blood EAA 0.6715       0.9357
## GrimAge EAA    0.8736       0.9357
## DNAmTL         0.6495       0.9357
## IEAA           0.8091       0.9357
## EEAA           0.9357       0.9357

Ambient Exposure

Linear regression (simple model)

In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

## X: Ambient Exposure Measurements:
##                                                  bap               pm25
## Horvath EAA                       -0.007    (0.0048) 0.0018    (0.0024)
## Hannum EAA                        -0.0023   (0.0042) 0.0001    (0.002 )
## PhenoAge EAA                      -0.002    (0.0045) -0.001    (0.0022)
## Skin&Blood EAA                    0.0029    (0.0034) -0.0005   (0.0017)
## GrimAge EAA                       0.0017    (0.0031) 0.0013    (0.0015)
## DNAmTL                            0         (0.0002) -0.0001   (0.0001)
## IEAA                              -0.0098*  (0.0042) 0.0015    (0.0022)
## EEAA                              -0.0012   (0.0054) 0.0003    (0.0026)
##                [P<0.001: ***; P<0.01: **; P<0.05: *]               <NA>
##                               ANY                BPE                BaA
## Horvath EAA    0.0009 ** (0.0003) -0.0072   (0.0046) -0.0042   (0.0029)
## Hannum EAA     0.0004    (0.0003) -0.0032   (0.004 ) -0.0015   (0.0026)
## PhenoAge EAA   0.0006 *  (0.0003) -0.0037   (0.0043) -0.0004   (0.0027)
## Skin&Blood EAA 0.0004    (0.0002) 0.0026    (0.0032) 0.0013    (0.0021)
## GrimAge EAA    0.0007 ***(0.0002) 0.0013    (0.0029) 0.0014    (0.0019)
## DNAmTL         0         (0     ) 0         (0.0002) 0         (0.0001)
## IEAA           0.0006 *  (0.0003) -0.0103*  (0.004 ) -0.0055*  (0.0026)
## EEAA           0.0006    (0.0003) -0.0023   (0.0051) -0.0007   (0.0032)
##                              <NA>               <NA>               <NA>
##                               BbF                BkF                CHR
## Horvath EAA    -0.0034   (0.003 ) -0.0197   (0.0133) -0.0034   (0.0032)
## Hannum EAA     -0.0017   (0.0026) -0.0082   (0.0117) -0.0017   (0.0028)
## PhenoAge EAA   -0.0009   (0.0028) -0.0063   (0.0124) -0.0001   (0.003 )
## Skin&Blood EAA 0.0014    (0.0021) 0.0063    (0.0094) 0.0013    (0.0022)
## GrimAge EAA    0.0013    (0.0019) 0.0058    (0.0085) 0.0019    (0.002 )
## DNAmTL         0         (0.0001) 0.0001    (0.0005) 0         (0.0001)
## IEAA           -0.0049   (0.0026) -0.0269*  (0.0117) -0.0048   (0.0028)
## EEAA           -0.0009   (0.0033) -0.0047   (0.0148) -0.0008   (0.0035)
##                              <NA>               <NA>               <NA>
##                               DBA                FLT                FLU
## Horvath EAA    -0.0193   (0.0117) -0.0061*  (0.0031) 0.0008    (0.0008)
## Hannum EAA     -0.0103   (0.0103) -0.0025   (0.0027) -0.0003   (0.0007)
## PhenoAge EAA   -0.0046   (0.011 ) -0.0008   (0.0029) 0.0007    (0.0007)
## Skin&Blood EAA 0.0043    (0.0083) -0.0002   (0.0022) 0.0005    (0.0005)
## GrimAge EAA    0.0044    (0.0075) 0.0012    (0.002 ) 0.0009    (0.0005)
## DNAmTL         -0.0001   (0.0005) 0.0001    (0.0001) 0         (0     )
## IEAA           -0.0267*  (0.0102) -0.0064*  (0.0027) 0.0006    (0.0007)
## EEAA           -0.0075   (0.0131) -0.0024   (0.0034) -0.0001   (0.0009)
##                              <NA>               <NA>               <NA>
##                               IPY                NAP                PHE
## Horvath EAA    -0.0112   (0.0082) 0.0002 ** (0.0001) 0.0006    (0.0005)
## Hannum EAA     -0.0031   (0.0072) 0.0001    (0.0001) -0.0001   (0.0004)
## PhenoAge EAA   -0.0054   (0.0077) 0.0001 *  (0.0001) 0.0005    (0.0005)
## Skin&Blood EAA 0.0055    (0.0058) 0.0001    (0     ) 0.0003    (0.0004)
## GrimAge EAA    0.0041    (0.0053) 0.0001 ***(0     ) 0.0006 *  (0.0003)
## DNAmTL         0.0001    (0.0003) 0         (0     ) 0         (0     )
## IEAA           -0.0173*  (0.0072) 0.0001 *  (0.0001) 0.0005    (0.0004)
## EEAA           -0.0021   (0.0092) 0.0001    (0.0001) 0         (0.0006)
##                              <NA>               <NA>               <NA>
##                               PYR
## Horvath EAA    -0.0054   (0.003 )
## Hannum EAA     -0.002    (0.0026)
## PhenoAge EAA   -0.0006   (0.0028)
## Skin&Blood EAA 0.0002    (0.0021)
## GrimAge EAA    0.0012    (0.0019)
## DNAmTL         0.0001    (0.0001)
## IEAA           -0.006 *  (0.0026)
## EEAA           -0.0018   (0.0034)
##                              <NA>

Urinary Measurements

Linear regression (simple model)

In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

## X: Urinary Measurements:
##                              Benzanthracene_Chrysene        Naphthalene
## Horvath EAA                       -0.0361   (0.1332) -0.0015*  (0.0006)
## Hannum EAA                        0.0486    (0.1146) -0.0012*  (0.0005)
## PhenoAge EAA                      -0.0481   (0.122 ) -0.0011   (0.0006)
## Skin&Blood EAA                    0.084     (0.0959) -0.0015***(0.0004)
## GrimAge EAA                       0.1136    (0.0845) 0         (0.0004)
## DNAmTL                            -0.0043   (0.005 ) 0         (0     )
## IEAA                              -0.1071   (0.1221) -0.001    (0.0006)
## EEAA                              0.0996    (0.1458) -0.0012   (0.0007)
##                [P<0.001: ***; P<0.01: **; P<0.05: *]               <NA>
##                2.Methylnaphthalene 1.Methylnaphthalene       Acenaphthene
## Horvath EAA     -0.0076   (0.0072)  -0.0105   (0.0175) 0.0223    (0.0388)
## Hannum EAA      -0.004    (0.0063)  -0.0191   (0.0149) 0.0363    (0.0332)
## PhenoAge EAA    -0.0096   (0.0066)  -0.021    (0.0159) 0.0897 *  (0.0348)
## Skin&Blood EAA  -0.0074   (0.0053)  -0.0251*  (0.0125) -0.0142   (0.0282)
## GrimAge EAA     0.0073    (0.0047)  0.0115    (0.0111) 0.0636 ** (0.0241)
## DNAmTL          -0.0003   (0.0003)  -0.0006   (0.0007) -0.0012   (0.0015)
## IEAA            -0.0049   (0.0067)  -0.0034   (0.016 ) 0.0423    (0.0355)
## EEAA            -0.0024   (0.008 )  -0.017    (0.0192) 0.0427    (0.0424)
##                               <NA>                <NA>               <NA>
##                Phenanthrene_Anthracene       Fluoranthene             Pyrene
## Horvath EAA         0.0031 *  (0.0015) 0.0312    (0.0176) 0.0731    (0.8107)
## Hannum EAA          0         (0.0013) 0.0012    (0.0153) 0.5231    (0.7025)
## PhenoAge EAA        0.0016    (0.0014) 0.0193    (0.0163) 1.1583    (0.7147)
## Skin&Blood EAA      0.0002    (0.0011) 0.0076    (0.0129) 0.4467    (0.5627)
## GrimAge EAA         0.0029 ** (0.0009) 0.0336 ** (0.011 ) 0.9093    (0.501 )
## DNAmTL              -0.0002** (0.0001) -0.002 ** (0.0007) -0.0252   (0.0306)
## IEAA                0.0026    (0.0014) 0.0252    (0.0162) 0.3662    (0.7378)
## EEAA                0.0005    (0.0017) 0.0062    (0.0195) 0.7881    (0.8911)
##                                   <NA>               <NA>               <NA>